• 경영과학
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• 제9권1호
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• pp.17-30
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• 1992

This paper is organized as follows. Section 2 illustrates several applications of neural networks. Section 3 presents the theoretical aspects of the major neural network paradigms as well as the structure of the back -propagation network used in the study. Section 4 describes the experiment including data analysis, modeling, and the performance criteria followed by the detailed discussion of the experimental results. Future research avenues including advantages and limitations of neural network are presented in the last section.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.143-143
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• 2000

A neural net based generalized predictive control(NNGPC) is presented for a hydraulic servo position control system. The proposed scheme employs generalized predictive control, where the future output being generated from the output of artificial neural networks. The proposed NNGPC does not require an accurate mathematical model for the nonlinear hydraulic system and takes less calculation time than GPC algorithm if the teaming of neural network is done. Simulation studies have been conducted on the position control of a hydraulic motor to validate and illustrate the proposed method.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(2)
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• pp.1266-1266
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• 2005

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(2)
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• pp.1067-1067
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• 2005

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2001년도 학술발표회 논문집(I)
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• pp.383-388
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• 2001

• 센서학회지
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• 제9권2호
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• pp.127-133
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• 2000

이 논문에서는 신경회로망을 이용하여 순환식 돈분처리 시스템의 실시간 모니터링을 궁극적으로 구현할 수 있는 새로운 방법을 제안하였다. 즉 미생물 군집내의 개체군밀도에 따른 각 처리조(유입수, 발효조, 폭기조, 1차 침전조 및 4차 침전조)에서의 폐수처리 과정을 모델을 시도하였다. 측정 데이터에 대해 우선 principle component analysis(PCA) 분석을 적용하여 각 처리조에서의 입력(미생물 밀도와 처리요소)과 출력간의 상관관계를 파악하고, 각각의 처리조마다 독립된 신경회로망을 적용하여 폐수처리 과정을 모델링하였다. 신경회로망의 입력으로 현재 탱크에서의 미생물의 개체군밀도를 직접 이용하는 대신 PCA 분석 결과를 이용함으로써, 비교적 적은 수의 데이터로 효과적인 모니터링 시스템을 구현할 수 있었다. 즉 각 처리조별로 학습된 신경회로망들을 연결하여 분석한 결과 2일 동안의 폐수 처리 변화를 비교적 정확히 예측할 수 있었다.

• 전기학회논문지
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• 제66권5호
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• 한국전산구조공학회논문집
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• 제19권2호
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• pp.125-138
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• 2006

교량의 손상추정을 위한 구조계 규명기법은 신호취득시스템 및 정보처리기술의 발전과 함께 최근에 많은 연구개발이 이루어지고 있다. 신경망기법이나 유전자 알고리즘과 같은 소프트컴퓨팅 기법은 뛰어난 패턴인식성능 때문에 손상추정 문제에 활발히 활용되고 있다. 본 연구에서는 모드계수를 활용한 신경망기법기반 손상추정을 수행하였으며, 신경망을 훈련시키기 위한 훈련패턴을 생성하는 해석모델에서의 불확실성을 효과적으로 고려할 수 있는 방법을 제시하였다. 해석모델의 불확실성 대하여 민감하지 않은 입력자료인 손상 전 후의 모드형상의 차 또는 모드형상의 비를 신경망의 입력자료로 활용하였다. 단 순보와 다주형교량에 대한 수치예제를 통하여 본 연구에서 제시한 기법의 타당성 및 적용성을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1807-1822
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• 2023

Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel. Formal signboards are placed with marketing objectives and are enriched with text for attracting people. Extracting and recognition of the text from natural images is an emerging research issue and needs consideration. When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult. Arabic language text scene extraction and recognition adds a number of complications and difficulties. The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations. The first stage uses a convolution autoencoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition. This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images. For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase. The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences. This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts. Our research after experimentations shows that the system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy. We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 추계학술대회 논문집
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• pp.127-128
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• 2008